ARTIFICIAL INTELLIGENCE PLATFORMS IN DENTAL CARIES DETECTION: A SYSTEMATIC REVIEW AND META-ANALYSIS

LYNDON P. ABBOTT, ANKITA SAIKIA, ROBERT P. ANTHONAPPA

Research output: Contribution to journalReview articlepeer-review

Abstract

Objectives: To assess Artificial Intelligence (AI) platforms, machine learning methodologies and associated accuracies used in detecting dental caries from clinical images and dental radiographs. Methods: A systematic search of 8 distinct electronic databases: Scopus, Web of Science, MEDLINE, Educational Resources Information Centre, Institute of Electrical and Electronics Engineers Explore, Science Direct, Directory of Open Access Journals and JSTOR, was conducted from January 2000 to March 2024. AI platforms, machine learning methodologies and associated accuracies of studies using AI for dental caries detection were extracted along with essential study characteristics. The quality of included studies was assessed using QUADAS-2 and the CLAIM checklist. Meta-analysis was performed to obtain a quantitative estimate of AI accuracy. Results: Of the 2538 studies identified, 45 met the inclusion criteria and underwent qualitative synthesis. Of the 45 included studies, 33 used dental radiographs, and 12 used clinical images as datasets. A total of 21 different AI platforms were reported. The accuracy ranged from 41.5% to 98.6% across reported AI platforms. A quantitative meta-analysis across 7 studies reported a mean sensitivity of 76% [95% CI (65% - 85%)] and specificity of 91% [(95% CI (86% - 95%)]. The area under the curve (AUC) was 92% [95% CI (89% - 94%)], with high heterogeneity across included studies. Conclusion: Significant variability exists in AI performance for detecting dental caries across different AI platforms. Meta-analysis demonstrates that AI has superior sensitivity and equal specificity of detecting dental caries from clinical images as compared to bitewing radiography. Although AI is promising for dental caries detection, further refinement is necessary to achieve consistent and reliable performance across varying imaging modalities.

Original languageEnglish
Article number102077
Number of pages20
JournalJournal of Evidence-Based Dental Practice
Volume25
Issue number1
Early online date11 Jan 2025
DOIs
Publication statusE-pub ahead of print - 11 Jan 2025

Fingerprint

Dive into the research topics of 'ARTIFICIAL INTELLIGENCE PLATFORMS IN DENTAL CARIES DETECTION: A SYSTEMATIC REVIEW AND META-ANALYSIS'. Together they form a unique fingerprint.

Cite this